ORiGAMi synthesizes sparse semi-structured mixed-type JSON data using path-encoded autoregressive tokenization and schema constraints, outperforming flattened tabular baselines on 17 of 18 fidelity, detection, and utility metrics while keeping privacy above 96%.
A comprehensive survey of synthetic tabular data generation
9 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Tabular data is one of the most prevalent and important data formats in real-world applications such as healthcare, finance, and education. However, its effective use in machine learning is often constrained by data scarcity, privacy concerns, and class imbalance. Synthetic tabular data generation has emerged as a powerful solution, leveraging generative models to learn underlying data distributions and produce realistic, privacy-preserving samples. Although this area has seen growing attention, most existing surveys focus narrowly on specific methods (e.g., GANs or privacy-enhancing techniques), lacking a unified and comprehensive view that integrates recent advances such as diffusion models and large language models (LLMs). In this survey, we present a structured and in-depth review of synthetic tabular data generation methods. Specifically, the survey is organized into three core components: (1) Background, which covers the overall generation pipeline, including problem definitions, synthetic tabular data generation methods, post processing, and evaluation; (2) Generation Methods, where we categorize existing approaches into traditional generation methods, diffusion model methods, and LLM-based methods, and compare them in terms of architecture, generation quality, and applicability; and (3) Applications and Challenges, which summarizes practical use cases, highlights common datasets, and discusses open challenges such as heterogeneity, data fidelity, and privacy protection. This survey aims to provide researchers and practitioners with a holistic understanding of the field and to highlight key directions for future work in synthetic tabular data generation.
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MbaGCN combines message aggregation, selective state space transitions, and node state prediction to create a more scalable deep graph convolutional network.
TabKDE generates synthetic tabular data using copula transformations followed by kernel density estimation, matching prior accuracy with negligible training time and reduced storage via coresets.
A framework using generative AI to produce synthetic multilevel data for Monte Carlo simulations that evaluate the performance and parameter recovery of quantitative methods.
SAGE improves LLM-based synthetic tabular data generation by enforcing sparse, value-adaptive dependency guidance, yielding higher fidelity and 10% better downstream F1 scores than prior methods.
PLAG boosts tabular anomaly detection by using pseudo-label-guided synthetic anomaly generation with a two-stage filter, achieving SOTA results and lifting F1 scores by 0.08-0.21 when added to existing detectors.
Seq. RC-TGAN adds a spectral envelope loss to RC-TGAN and uses VGM discretization plus simulated benchmarks with known envelopes to generate relational time series that better match frequency-domain features.
TabFORGE generates high-quality synthetic tabular data by leveraging pretrained causality-aware representations in a two-stage diffusion-decoder architecture that mitigates latent distribution shifts.
Synthetic data generation in privacy-constrained medical domains shifts the engineering challenge from data availability to the elicitation, validation, and evolution of stakeholder-specific validity properties.
citing papers explorer
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Autoregressive Synthesis of Sparse and Semi-Structured Mixed-Type Data
ORiGAMi synthesizes sparse semi-structured mixed-type JSON data using path-encoded autoregressive tokenization and schema constraints, outperforming flattened tabular baselines on 17 of 18 fidelity, detection, and utility metrics while keeping privacy above 96%.
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Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space
MbaGCN combines message aggregation, selective state space transitions, and node state prediction to create a more scalable deep graph convolutional network.
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TabKDE: Simple and Scalable Tabular Data Generation with Kernel Density Estimates
TabKDE generates synthetic tabular data using copula transformations followed by kernel density estimation, matching prior accuracy with negligible training time and reduced storage via coresets.
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Generative AI-Based Monte Carlo Simulation for Method Evaluation Using Synthetic Multilevel Data
A framework using generative AI to produce synthetic multilevel data for Monte Carlo simulations that evaluate the performance and parameter recovery of quantitative methods.
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SAGE: Sparse Adaptive Guidance for Dependency-Aware Tabular Data Generation
SAGE improves LLM-based synthetic tabular data generation by enforcing sparse, value-adaptive dependency guidance, yielding higher fidelity and 10% better downstream F1 scores than prior methods.
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Enhancing Tabular Anomaly Detection via Pseudo-Label-Guided Generation
PLAG boosts tabular anomaly detection by using pseudo-label-guided synthetic anomaly generation with a two-stage filter, achieving SOTA results and lifting F1 scores by 0.08-0.21 when added to existing detectors.
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Sequential RC-TGAN: Generating Relational Time Series with Spectral Envelope Loss
Seq. RC-TGAN adds a spectral envelope loss to RC-TGAN and uses VGM discretization plus simulated benchmarks with known envelopes to generate relational time series that better match frequency-domain features.
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Tabular Foundation Model for Generative Modelling
TabFORGE generates high-quality synthetic tabular data by leveraging pretrained causality-aware representations in a two-stage diffusion-decoder architecture that mitigates latent distribution shifts.
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Property-Driven Synthetic Data Engineering for Data-Scarce Software Systems: Reflections from the Breast Cancer Domain
Synthetic data generation in privacy-constrained medical domains shifts the engineering challenge from data availability to the elicitation, validation, and evolution of stakeholder-specific validity properties.